||We develop methods for data acquisition, analysis, modeling and visualization of performance parameters in endurance sports with emphasis on competitive cycling. For this purpose, we designed a simulator to facilitate the measurement of training parameters in a laboratory environment, to familiarize cyclists with unknown tracks, and to develop models for training control and performance prediction.
The simulator is based on a Cyclus2 ergometer (RBM Elektronik-Automation GmbH, Leipzig, Germany), which provides a realistic cycling experience since one can mount arbitrary bikes and its elastic suspension even allows for a sway pedal stroke. The eddy current brake guarantees non-slipping transmission of a braking resistance up to 3000 W.
Operating the Cyclus2 in the gradient mode, we impose arbitrary slopes by our own platform independent PC-based control software at a sampling rate of 2 Hz. The height profiles for various tracks were recorded using a commercial GPS device.
The Cyclus2 has two major constraints with respect to simulating real tracks: We must focus on tracks without downhill accelerations since it has no engine and the eddy current brake requires a minimum rotation velocity of the flywheel to accurately generate the brake force. Therefore, we fixed the derailleurs to a heavy gear and mounted four electronic buttons to the handlebar which act like shift levers of virtual gears. Our software incorporates the virtual gear into the gradient so that the cyclist feels a correct resistance while the flywheel exceeds the minimum rotation velocity in all realistic uphill scenarios. Moreover, we can simulate arbitrary gears easily and record them over time. As the physical flywheel rotation is faster than in the simulation, our software must correct the related performance data. The simulation includes a video playback that is synchronized with the cyclist’s current position on the track. In addition, time, distance, speed, cadence, heart rate, power and gears are monitored, a 2D-projection of the course gives feedback on the progress and a gradient profile indicates the slope in the surrounding of the current position.
Comparative outdoor tests with an SRM power meter (Schoberer Rad Messtechnik, Welldorf, Germany) show that the simulator gives reasonable estimates for different pacing strategies (constant power/speed/heart rate).
In future, we strive to integrate a more precise mechanical model (Martin et al., 1998), extend the palette of physiological measurements (oxygen consumption, ECG, lactate etc) and implement models for these parameters. The whole system shall indicate the optimum pacing strategy as Gordon derived in 2005 for simple models and synthetic data. Using sophisticated biofeedback visualization, cyclists shall be able to optimally prepare themselves even for unfamiliar tracks on our simulator.